Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health.

ABSTRACT: Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear magnetic spectroscopy, and the use only of conventional statistical methods. To overcome these challenges, we applied liquid chromatography-tandem mass spectrometry and measured >1000 metabolites in the METSIM study including 10,197 men. We applied the machine learning approach together with conventional statistical methods to identify metabolites associated with all-cause mortality. The three independent machine learning methods (logistic regression, XGBoost, and Welch's t-test) identified 32 metabolites having the most impactful associ

SUBMITTER: Oravilahti A 

PROVIDER: S-EPMC11546733 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature